MyBehavior: Persuasion by Adapting to User Behavior and User Preference
1 other identifier
interventional
17
1 country
1
Brief Summary
MyBehavior is a mobile application with a suggestion engine that learns a user's physical activity and dietary behavior, and provides finely-tuned personalized suggestions. To our knowledge, MyBehavior is the first smartphone app to provide personalized health suggestions automatically, going beyond commonly used one-size-fits-all prescriptive approaches, or tailored interventions from health-care professionals. MyBehavior uses an online multi-armed bandit model to automatically generate context-sensitive and personalized activity/food suggestions by learning the user's actual behavior. The app continually adapts its suggestions by exploiting the most frequent healthy behaviors, while sometimes exploring non-frequent behaviors, in order to maximize the user's chance of reaching a health goal (e.g. weight loss).
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at below P25 for not_applicable
Started May 2013
Shorter than P25 for not_applicable
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
May 1, 2013
CompletedPrimary Completion
Last participant's last visit for primary outcome
June 1, 2013
CompletedStudy Completion
Last participant's last visit for all outcomes
June 1, 2013
CompletedFirst Submitted
Initial submission to the registry
February 2, 2015
CompletedFirst Posted
Study publicly available on registry
February 10, 2015
CompletedFebruary 11, 2015
February 1, 2015
1 month
February 2, 2015
February 10, 2015
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
User intentions to follow automated suggestions and behavior change
The primary outcome is to measure efficacy of MyBehavior suggestions. Efficacy will be measured in two dimensions (1) whether users intend to follow the automated suggestions from MyBehavior (2) effectiveness of automated suggestions in actual behavior change. User intentions towards following MyBehavior suggestions are measured using a 5 point likert scale. The investigators will ask users to rate whether they can follow the suggestions on an average day within a scale of 1-5 (1- I can't follow the suggestion, 5 - I can easily follow the suggestion). On the other hand, behavior change is measured from food (calories in per meal consumed) and activity (walking, running or exercise durations per day etc.) log collected using their smartphone. Regarding physical activity, how much physical activity users are performing will be compared across experiment conditions. Similarly, calorie consumption change in food will be used to compare dietary behavior change.
3 weeks
Secondary Outcomes (1)
Usability improvements of automated suggestions
3 weeks
Study Arms (2)
Generic suggestions
ACTIVE COMPARATORControl group participants received suggestions generated by the a nutritionist and exercise trainer. These suggestions didn't relate to user's life or their past behavior.
MyBehavior
EXPERIMENTALExperiment group participants received personalized suggestions from MyBehavior that relates their life and past behavior.
Interventions
The intervention automatically provides personalized suggestions based on users behavior and user context. Suggestions relates to users life and how often they have done them in the past. Since the suggestions relate to users' lives, they are easy to follow.
A nutritionist and an exercise trainer jointly created 45 food and exercise suggestions based on guidelines posted by the NIH. These suggestions ask users to walk for 30 minutes or eat healthier foods. These suggestions however doesn't personalize to users daily behavior into account.
An Android Smartphone with operating system version higher than 2.2
Eligibility Criteria
You may qualify if:
- In relatively healthy condition. Also, users must be interested in health and fitness.
You may not qualify if:
- Individuals with physical disability and dietary problems are excluded.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Cornell University
Ithaca, New York, 14850, United States
Related Publications (1)
Rabbi M, Pfammatter A, Zhang M, Spring B, Choudhury T. Automated personalized feedback for physical activity and dietary behavior change with mobile phones: a randomized controlled trial on adults. JMIR Mhealth Uhealth. 2015 May 14;3(2):e42. doi: 10.2196/mhealth.4160.
PMID: 25977197DERIVED
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Mashfiqui Rabbi, BS
Cornell University
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- RANDOMIZED
- Masking
- SINGLE
- Who Masked
- PARTICIPANT
- Purpose
- PREVENTION
- Intervention Model
- PARALLEL
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
February 2, 2015
First Posted
February 10, 2015
Study Start
May 1, 2013
Primary Completion
June 1, 2013
Study Completion
June 1, 2013
Last Updated
February 11, 2015
Record last verified: 2015-02